Technology Stack & Architecture
1. High-Level Architecture
Clients
- Flutter (iOS + Android) for mobile apps.
- React (SPA/SSR Hybrid) web application with Tailwind CSS.
- React-based dashboards for Admins and Providers.
API Layer
- API Gateway (Ingress) → BFFs for Mobile & Web (Node.js / NestJS) → Microservices.
Microservices (Dockerized & Kubernetes-deployed):
- Identity & Authentication
- Pets & EHR (Electronic Health Records)
- Bookings & Scheduling
- Tele-vet Consultations
- Marketplace
- Payments & Subscriptions
- AI & Retrieval Systems
- Recommendations Engine
- Messaging & Moderation
- Notifications
- Places & Maps
- Analytics
Data Plane
- PostgreSQL (OLTP)
- Redis (cache, rate-limiting, queues)
- MinIO / S3 (media storage)
- pgvector / Qdrant (vector storage)
- OpenSearch / Meilisearch (search)
- ClickHouse / BigQuery (analytics)
AI Layer
- Python FastAPI Orchestrator for RAG, safety filters, and model adapters.
- Supports OpenAI, Anthropic, Hugging Face, and private LLMs.
CI/CD & Infrastructure
GitHub Actions → Docker → Kubernetes (EKS/GKE/AKS) or ECS Fargate.
2. Long-Term Technical Stack (10-Year View)
Stack Overview
| Layer | Primary Recommendation | Rationale / Notes | ||
|---|---|---|---|---|
| Mobile | Flutter (latest stable) | Single codebase, native performance, strong UI parity. | ||
| Web Frontend | React.js + Tailwind CSS | Ecosystem maturity, SSR/ISR with Next.js, design speed. | ||
| Backend | Node.js + NestJS | TypeScript-first, modular, scalable microservices. | ||
| API Styles | REST (OpenAPI) + GraphQL (optional) | REST for simplicity, GraphQL for complex client needs. | ||
| Containerization | Docker | Standard portability. | ||
| Database | PostgreSQL | Extensible, JSONB support, scalable. | ||
| Cache/Queue | Redis | Low-latency, streams, pub/sub. | ||
| Search | OpenSearch or Meilisearch | OpenSearch for scale, Meili for dev agility. | ||
| Vector DB | pgvector (Postgres extension) / Qdrant | pgvector for simplicity, Qdrant for scale. | ||
| Object Storage | S3 (AWS) / MinIO | Durable media storage. | ||
| AI Orchestration | Python FastAPI + model adapters | Strong ML ecosystem, RAG orchestration. | ||
| Realtime/Video | WebRTC via Agora/Twilio/Daily | HIPAA-like support, low latency. | ||
| Payments | Stripe (+ PayPal) | Robust subscriptions, global payouts. | ||
| Auth | OAuth2 + OIDC, JWT, RBAC | Industry-standard security. | ||
| CI/CD | GitHub Actions | Native GitHub integration. | ||
| Testing | Jest/Vitest, Cypress, Flutter test, Playwright | Multi-layer automated testing. | ||
3. Detailed Technical Features
3.1 Mobile (Flutter)
- State Management: Riverpod / BLoC
- Networking: Dio with interceptors, retries
- Local DB: Isar or Hive + Encrypted Storage
- Push Notifications: FCM + APNs
- Maps/GPS: Google Maps + Geofencing
- Video: Flutter WebRTC + Agora/Twilio SDK
- Analytics: Firebase / Posthog
- DevOps: CI/CD with GitHub Actions & Fastlane
3.2 Web Frontend (React + Tailwind)
- Framework: React 18+ with Next.js (for SEO)
- Styling: Tailwind CSS + Radix UI / Headless UI
- State Management: React Query + Zustand/Redux
- Forms & Validation: React Hook Form + Zod
- Accessibility: ARIA roles, CI axe-core checks
- Performance: Code-splitting, optimized images, Core Web Vitals
3.3 Backend (Node.js + NestJS)
- Architecture: Modular microservices, BFFs for mobile/web
- DB Layer: Prisma ORM with migrations
- Messaging/Queues: BullMQ (Redis)
- Security: OAuth2, JWT, Helmet, CORS, CSP
- Docs: OpenAPI via Nest Swagger
- Patterns: Outbox Pattern for reliable event-driven systems
3.4 AI/ML Layer (Python FastAPI)
- RAG Orchestration (retrieval-augmented generation)
- Adapters: OpenAI, Anthropic, HuggingFace, local LLMs
- Moderation: Text/image safety filters
- Logging: Prompt & response storage with explainability
- Latency Target: p95 < 1.7s
3.5 Data & Storage
- Postgres (primary DB) → DB-per-microservice
- Redis: sessions, queues, rate limiting
- OpenSearch: aggregations, analytics queries
- Meilisearch: lightweight dev search
- Qdrant / pgvector: vector similarity & AI retrieval
3.6 Realtime & Telehealth
- Video: WebRTC via Agora/Twilio/Daily
- Chat: Socket.io/uWebSockets + Redis Pub/Sub
- Recording: Session storage in S3 (with consent)
3.7 Payments & Subscriptions
- Stripe + PayPal: subscriptions, one-time payments, payouts
- Webhooks: dedicated microservice with idempotency keys
- PCI Compliance: secure tokenization
3.8 Search & Recommendation
- ANN (HNSW) in Qdrant/Milvus for recommendations
- Two-tower embedding models (user vector vs item vector)
- LLM re-ranker for personalization
4. Technical Trade-offs
- Flutter vs Native: Flutter chosen for speed & design parity.
- Node.js vs Laravel: Node.js + TypeScript ensures unified stack and smoother AI integrations.
- pgvector vs Qdrant: Start simple (pgvector), scale to Qdrant when needed.
- Kubernetes vs Serverless: K8s for long-term stability; FaaS selectively.
5. Security & Compliance
- TLS 1.3 everywhere
- Cloudflare / AWS WAF for security
- Secrets via Vault/KMS
- Immutable audit logs
- GDPR & data residency compliance
6. DevOps & CI/CD
- Feature branch → PR (unit, lint, type-check)
- Merge → full suite + integration tests
- Canary deploy to staging → smoke/performance tests
- ArgoCD/Flux for production rollouts
7. Roadmap
- Phase 0 (MVP): Flutter, React, core microservices (Auth, EHR, Tele-vet).
- Phase 1 (Scale): Add search, payments, onboarding flows.
- Phase 2 (AI-rich): Full AI orchestrator, advanced moderation, re-rankers.
- Phase 3 (Global): Multi-region DBs, i18n, marketplace expansion.
8. Cost & Ops Considerations
- AI Costs: tiered usage — small classifiers free tier, large LLMs for premium.
- Infra Costs: start with managed (RDS, Fargate, Qdrant Cloud), optimize later.
Team Size (early phase): 2–4 backend, 2–3 frontend, 2 mobile, 1–2 AI/ML, 1–2 DevOps, 1–2 QA.